Quality improvement outcomes from the introduction of a geriatrician into a rehabilitation setting
Bibliographic record
Abstract
OBJECTIVES: Geriatrician impact on patient and system outcomes in formal rehabilitation settings has not been well described to date. We studied the effect of adding a geriatric medicine consultation service to a geriatric focused rehabilitation setting providing care to dialysis and non-dialysis patients. DESIGN/SETTING/PARTICIPANTS: A pre- and post-retrospective observational cohort study from January 1, 2009 to June 30, 2019 on all consecutively admitted adults aged 65 and older to general rehabilitation program, and adults aged 60 and older to specialized dialysis rehabilitation program, within a 25 bed general rehabilitation unit in a large urban academic rehabilitation center in Toronto, Ontario. Data were analyzed with quality improvement methodology including Statistical Process Control charts (XmR and U charts). INTERVENTION: Addition of a geriatric medicine service providing automatic comprehensive geriatric assessment and co-management consultative services for all admitted patients from admission onwards who met criteria for the intervention. The intervention commenced on August 1, 2013. MEASUREMENTS: Outcome measures were length of stay (days), service interruption frequency, and average functional independence measure (FIM) change (discharge FIM minus admission FIM) which uses the validated FIM score, a marker of functional ability. A 22 point change in FIM score is clinically relevant. RESULTS: Patient characteristics: general rehabilitation patients (n = 1395, mean age = 79.7, 50.1% female) and dialysis rehabilitation patients (n = 838, mean age = 72.8, 41.8% female). The average FIM change following intervention improved from 20.8 to 29.3 in the general rehabilitation cohort (40.6% improvement, SD = 5.51) and from 22.1 to 30.6 in the dialysis rehabilitation cohort (38.6% improvement, SD = 5.88). Changes in length of stay (24.9%-28.1% reduction) and service interruption frequency (34.3%-49.7% reduction) were also observed. CONCLUSION: Introduction of a geriatric medicine service for rehabilitation inpatients was associated with significant FIM score improvements. Our results suggest this intervention contributes to important gains in functional independence in reduced time for older adults receiving inpatient rehabilitative care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".